These concepts are diverse, coming from different perspectives with different methods and archetypal case studies. But there are shared themes: a focus on more diffuse and polycentric urban forms; recognition of city connections across multiple scales; and the rise of ever larger urban regions embedded in thicker global networks.

Representing and exploring the diversity of contemporary global urban forms is a challenge for cartographers. We often focus on mapping the amazing richness and diversity of dominant global cities like London and New York. Yet this is clearly a very biased lens from which to frame the vast majority of the globe, as researchers have noted. Postcolonial critiques like Robinson’s ordinary cities (2006) argue for a much more representative and cosmopolitan comparative urbanism. From a different angle, provocative research like Brenner’s (2014) ‘planetary urbanism‘ has critiqued the contentions of a universal urban age, arguing that urban/rural distinctions are no longer meaningful where capitalist networks reach to every corner of the globe.

I recently released an interactive map of the new Global Human Settlement Layer (GHSL) produced by the European Commission JRC and CIESIN Columbia University. This dataset makes several advances towards an improved cartography of the diversity of global urbanism. Firstly it is truly global, representing all the world’s landmass and settlements at a higher level of detail, down to 250m. Secondly the population density and built-up layers are continuous: there are no inherent city boundaries or urban/rural definitions (the GHSL includes an additional layer with urban centres defined, but the user can ignore these and create their own boundaries from the underlying layers). Thirdly the dataset is a time-series, including 1975, 1990, 2000 and 2015. Finally the data layers and the methods used to create them are fully open.

Diversity and Structure of Global Urban Constellations
The complexity and scale of the GHSL data is both beautiful and beguiling. In China and India there are continuous landscapes of connected urban settlements with hundreds of millions of people, scattered across many thousands of square kilometres. The cartographic appearance of these regions is like constellations of stars coalescing in vast nebulae of diffuse population. Densities of South and South-East Asian towns and small settlements in semi-rural regions exceed many major cities in Europe and North America. These are complex evolving landscapes at a scale and extent unprecedented in the history of urbanism.

Similarly there are unique trends in other major regions of urbanisation such as Latin America. Here major centres are very high density, but the extent of diffuse rural populations is far less prevalent. As a result countries like Colombia and Brazil have some of the highest urban population densities in the world.

The recognition of this global diversity does not mean abandoning global theories of urbanism. Even amongst such complexity and diversity, we can still observe shared spatial patterns and connections. Clearly we are observing landscapes heavily influenced by our current era of intense globalisation, as well as retaining inherited patterns from previous eras. Spatial logics of globalisation are apparent across the globe, though differentiated between regions, economies and societies.

The pull of coastal areas for global trade is an obvious spatial pattern. The importance of port cities is also applicable to historic periods of ancient civilisations, and indeed to globalisation in the 18th and 19th centuries. But the difference in the 20th and 21st centuries appears to be the more intensive links between major ports and global megaregions of production and manufacturing. We can observe this in the huge megaregions of China: the Pearl River Delta and Yangtze Delta (both with around 50m population depending on where the boundary is drawn), which are China’s leading manufacturing centres.

It also applies to Europe, with the higher density spine of the ‘blue banana’ linking low country ports to manufacturing centres in western Germany and north-eastern France, and more loosely to south-east England and northern Italy. As well as the manufacturing roles, it is clear that most major global financial centres are closely linked to megaregions, either at their core (e.g. Shanghai, New York, Tokyo) or within a couple of hours travel (e.g. Hong Kong, London, Paris). These centres provide the capital and business services that embed megaregions in global networks.

The importance of ports is also evident in South Asia. Port cities in South Asia are amongst the fastest growing in the world, such as Dhaka, Mumbai, Karachi, Kolkata and Chennai. But megaregions here appear as yet to be less extensive and well connected. Latin American cities are even more spatially separated and precisely defined in density terms, though there are signs of increasing connections between for example the two great Brazilian metropolises, Sao Paulo and Rio de Janeiro, and in the north between Venezuelan and Colombian port cities.

Another fascinating pattern relates to large previously rural areas of population in developing countries that are urbanising in more diffuse and bottom-up patterns. McGee used the term desakota (village-city) to describe patterns of disperse rural development in Java Indonesia. There appear to be similar patterns emerging across regions of China and India, including many areas of the vast Ganges plain, and along the great rivers of China. One of most striking features in China is the concentration of semi-rural and urban populations radiating south-west from Beijing towards Shijiazhuang and then south towards Zhengzhou (this follows one of China’s oldest rail routes, built 1903 and is nearly 600km long).

There are several areas of sub-Saharan Africa where desakota-like patterns seem to be apparent. The west coast around Nigeria and Ghana is one such area. Another is the many developments around Lake Victoria in Uganda, Kenya and Tanzania. Clearly the cultural and geographical diversity is very high in these regions, and my own knowledge of these countries is very limited. But the similar density patterns is still of interest.

Population and Density StatisticsThe World Population Density map includes density statistics at national and city scales, with population totals classified into density groups (turn on the Interactive Statistics button at the top left). These help to identify differences in patterns of settlement, and how city densities relate to national distributions.

If we view the world’s highest density cities, we can see the clear links to the above discussion of urbanisation in South Asia and East Asia, and major global port cities. Note however there are many issues with defining and measuring density, which need to be borne in mind when interpreting such statistics. These are measures of residential density, and results will likely be affected by the scale and accuracy of the underlying census data. It would also be better statistically to measure peaks as the 95th or 99th percentile to prevent a single square km cell skewing the results, as there are some outliers in the results.

Highest peak density cities GHSL 2015 1km scale-

City Name

Country

Peak Density (000s pp/km2)

Mean Density (000s pp/km2)

Population (millions)

Xiamen-Longhai

China

330.5

6.3

4.75

Peshawar

Pakistan

228.9

3.3

7.54

Dhaka

Bangladesh

197.8

9.1

24.83

Daegu

South Korea

189.4

8.5

2.58

Maunath Bhanjan

India

177

38.4

0.77

Cairo

Egypt

175.5

5.1

37.84

Kolkata

India

173.5

5.8

26.87

Baharampur

India

166.1

38

1.25

Bahawalpur

Pakistan

136.9

29.6

1.06

Xi’an

China

135.4

7.1

6.04

Kabul

Afghanistan

132.7

18

4.36

Nanjing

China

130.1

6.7

6.6

Guangzhou-Shenzhen

China

128.3

5.6

46.04

Hangzhou-Shaoxing

China

127.6

4.4

7.81

Manila

Philippines

127

9.9

22.45

We can also consider the highest population city-regions based on the GHSL urban centre boundaries. These are defined as continuous built-up areas, with polycentric regions linked into single cities. This leads to quite different results for world’s largest cities, with the Pearl River Delta measured as the world’s biggest urban agglomeration at 46 million (and that’s not including Hong Kong or Macao). It is interesting to compare this to results from the UN World Urbanisation Prospects data, which keeps these regions as separate cities and identifies Tokyo as the world’s largest city-region.

Highest population urban centres GHSL 2015 1km scale-

City Name

Country

Peak Density (000s pp/km2)

Mean Density (000s pp/km2)

Population (millions)

Guangzhou-Shenzhen

China

128.3

5.6

46.04

Cairo

Egypt

175.5

5.1

37.84

Jakarta

Indonesia

20.4

6.1

36.4

Tokyo

Japan

23

6.2

33.74

Delhi

India

68

11.1

27.63

Kolkata

India

173.5

5.8

26.87

Dhaka

Bangladesh

197.8

9.1

24.83

Shanghai

China

104.4

7.5

24.67

Mumbai

India

49.5

13.9

23.41

Manila

Philippines

127

9.9

22.45

Seoul

South Korea

103.1

8.8

22.13

Mexico City

Mexico

42

8.2

20.09

SÃ£o Paulo

Brazil

38.7

8.9

20.02

Beijing

China

84.8

6.6

19.9

Osaka

Japan

13.4

5

16.53

Future Cartography of Global Urbanism
Population density is clearly a very useful base from which to understand urbanisation and patterns of settlement. But we can also see its limitations too in the World Density Map if urbanisation is viewed only in terms of density. Many US city-regions are very low density, much lower than semi-rural parts of Asia and Africa, but these US cities are amongst the most affluent and highly urbanised areas of the globe.

Clearly a more comprehensive cartography of global urbanism would combine population density with measurements of development and economic activity, and the flows of people, goods, energy and information that describe the dynamics of how cities and networks function. The development of open global datasets like the GHSL will greatly help in these endeavours.

Another important issue is improving the sophistication of spatial statistics to include multiple urban boundaries and limit Modifiable Areal Unit effects. This would be possible with the GHSL dataset, and I have tried including national and city statistics, but clearly MAUP effects remain when using fixed city boundaries. Something along the lines of my colleagues’ research testing statistics for multiple boundaries simultaneously and showing their influence would be a good avenue to explore.

A brilliant new dataset produced by the European Commission JRC and CIESIN Columbia University was recently released- the Global Human Settlement Layer (GHSL). This is the first time that detailed and comprehensive population density and built-up area for the world has been available as open data. As usual, my first thought was to make an interactive map, now online at- http://luminocity3d.org/WorldPopDen/

The World Population Density map is exploratory, as the dataset is very rich and new, and I am also testing out new methods for navigating statistics at both national and city scales on this site. There are clearly many applications of this data in understanding urban geographies at different scales, urban development, sustainability and change over time. A few highlights are included here and I will post in more detail later when I have explored the dataset more fully.

The GHSL is great for exploring megaregions. Above is the northeastern seaboard of the USA, with urban settlements stretching from Washington to Boston, famously discussed by Gottman in the 1960s as a meglopolis.

Europe’s version of a megaregion is looser, but you can clearly see the corridor of higher population density stretching through the industrial heartland of the low countries and Rhine-Ruhr towards Switzerland and northern Italy, sometimes called the ‘blue banana’.

The megaregions of China are spectacularly highlighted, above the Pearl River Delta including Guangzhou, Shenzhen and Hong Kong amongst many other large cities, giving a total population of around 50 million.

The Yangtze Delta is also home to another gigantic polycentric megaregion, with Shanghai as the focus. Population estimates range from 50-70 million depending on where you draw the boundary.

The form of Beijing’s wider region is quite different, with a huge lower density corridor to the South West of mixed industry and agriculture which looks like the Chinese version of desakota (“village-city”) forms. This emerging megaregion, including Tianjin, is sometimes termed Jingjinji.

The term desakota was originally coined by McGee in relation to Java in Indonesia, which has an incredible density of settlement as shown above. There are around 147 million people living on Java.

The intense settlement of Cairo and the Nile Delta is in complete contrast to the arid and empty Sahara.

Huge rural populations surround the delta lands of West Bengal and Bangladesh, focused around the megacities of Kolkata and Dhaka.

There is a massive concentration of population along the coast in South India. This reflects rich agriculture and prospering cities, but like many urban regions is vulnerable to sea level changes.

The comprehensive nature of the GHSL data means it can be analysed and applied in many ways, including as a time series as data is available for 1975, 1990, 2000 and 2015. So far I have only visualised 2015, but have calculated statistics for all the years (turn the interactive statistics on at the top left of the website- I’ll post more about these statistics later). Change over time animations would definitely be an interesting approach to explore in the future. Also see some nice work by Alasdair Rae who has produced some excellent 3D visualisations using GHSL.

Recent urban growth in the UK has further emphasised the role of cities in influencing economic prosperity, quality of life and sustainability. If we are to meet 21st century social and economic challenges then we need to plan and run our cities better. Data analysis can play a useful role in this task by helping understand current patterns and trends, and identifying successful cities for sharing best practice.

Taking for example employment density change in northern English cities as shown below. Current growth is mainly in ‘knowledge-economy’ services that generally favour being clustered together in city centres, generally reinforcing a select few larger centres rather than many smaller centres. There is clear growth in Manchester, Leeds and Liverpool city centres, particularly Manchester which displays the biggest increase in employment density of any location in GB. But around these success stories there is a much more mixed picture of growth and decline for many other centres that are finding it more difficult to compete for firms and jobs.

Employment density change in the north of England (blue is an increase and orange decline). Manchester and Leeds city centres have established themselves as the largest centres, with the biggest increase in Manchester.

Interactive City Statistics

City statistics are available to make more precise comparisons between urban areas. Statistics can be viewed on LuminoCity3D.org by moving your mouse pointer over a city of interest, or by hovering/clicking on the GB Overview Chart at the bottom left of the screen. The graphs and statistics change depending on the map indicator selected, so that the LuminoCity maps and statistics are interactively integrated.

The example below shows public transport travel, a key sustainability indicator that also has important economic and equity implications. Greater London is by far the public transport centre of the UK with nearly 50% of commuting by public transport. Without the investment and historic advantages of London, city-regions like Manchester and Birmingham do not even manage 20% PT commuting. But we can see that it is not essential to be as gigantic as London to achieve more sustainable travel. Edinburgh, with a compact form and extensive publicly owned bus network, achieves 36% PT commuting.

Public transport commuting in central Scotland. Hovering over urban areas highlights indicator statistics and highlights the city’s position on the GB Chart.

All the datasets used are government open data. Websites such as LuminoCity would not be possible without recent open data initiatives and the release of considerable government data into the public domain. Links to the specific datasets used in each map are provided to the bottom right of the page under “Source Data”.

UK cities have been undergoing significant change over the last decade, and the 2011 census data provides a great basis for tracking current urban structure. I’ve mapped population and employment density for all of England and Wales in 2011, using a 1km2 grid scale approach-

The main themes that emerge are the dramatic intensification of London, high densities in some medium sized cities such as Leicester and Brighton, and the regeneration of the major northern conurbations, with Manchester and Birmingham as the largest employment hubs outside of London.

Mapping all of England and Wales together is a useful basis for considering city-regions and their connections (note Scotland has not yet published census 2011 employment data and is not mapped). Certainly this is a major theme in current policy debates grappling with the north-south divide and proposed high-speed rail links. I’ll be looking at densities in relation to network connections in future posts as this topic is part of ongoing research at CASA as part of the MECHANICITY project.

It is also possible to directly map changes in density between using the same visualisation approach (note the grid height describes density in 2011, while colour describes change in density between 2001-2011)-

The change map really highlights the pattern of city centre intensification combined with static or marginally declining suburbs in England and Wales. This trend was discussed in a previous post. The two statistics of peak and average densities reinforce the city centre versus suburbs divide, with peak density measurements growing much more than average densities. But the peak density statistic is somewhat unreliable (such as in the case of Birmingham/West Midlands) and we will be doing further work at CASA to define inner cities and produce more robust statistics of these trends.

Notes on the Analysis Method-

The density values were calculated from the smallest available units- Output Area population and Workplace Zone employment data from the 2011 census. This data was transformed to a 1km2 grid geography using a proportional spatial join approach, with the intention of standardising zone size to aid comparability of density measurements between cities. The transformation inevitably results in some MAUP errors. These are however minimised by the very fine scale resolution of the original data, which is much smaller than the grid geography in urban areas.

The workplace zone data is a very positive new addition by the Office for National Statistics for the 2011 census. There is a lot of new interesting information on workplace geography- have a look at my colleague Robin Edward’s blog, where he has been mapping this new data.

Defining city regions is another boundary issue for these statistics. I’ve used a simple approach of amalgamating local authorities, as shown below-

It’s been 14 years since the landmark Urban Task Force report, which set the agenda for inner-city densification and brownfield regeneration in the UK. Furthermore we’ve seen significant economic and demographic change in the last decade that’s greatly impacted urban areas. We can now use the 2011 census data, mapped here on the LuminoCity GB site, to investigate how these policies and socio-economic trends have transformed British cities in terms of population density change.

The stand-out result is that there’s a striking similarity across a wide range of cities, with overall growth achieved through high levels of inner-city densification (shown in lighter blue to cyan colours) in combination with a mix of slowly growing and moderately declining suburbs (dark purple to magenta colours).

We can see this pattern in the growing urban regions of Manchester, Birmingham, Leeds and Sheffield above. Manchester has the fastest population growth after London, with 8.1% growth in the city-region, and a massive 28% growth in the core local authority. Average densities in Manchester have gone up by 28% (+35 residents per hectare), but it’s not a uniform growth. There are new development sites at a very high 300 or 400 residents per hectare, contrasting with low density surrounds and the extensive remaining brownfield sites. There is a patchy nature to the current urban fabric of Manchester, indicating that much further development could still take place.

The West Midlands Conurbation is the third fastest growing city-region at 7.3%, with a higher 10% growth in the core city authority Birmingham. Density increases are more modest here (+13 residents per hectare) but the same general pattern remains. Similar patterns of high density inner-city growth are also clear in Leeds (5% growth) and Sheffield (8% growth).

Scottish cities have a stronger tradition of high density inner-city living. With compact cores already in place, Edinburgh (+6.5%) and Aberdeen (+5%) have been expanding the inner city into Leith and Old Aberdeen-

Meanwhile the UK’s former industrial powerhouses of Glasgow, Liverpool and Newcastle display a more problematic variation on this pattern. City centre intensification is still much in evidence, with core city authority populations growing at 8% in Newcastle, 6% in Liverpool and 4% in Glasgow. But this growth is in combination with outright decline in some surrounding towns and suburban areas, particularly around Glasgow. These patterns are linked to major programmes to overhaul poor inner-city housing stock, but are also inevitably linked to weaker economic growth in Glasgow and Liverpool. The picture is better in Tyne & Wear, where there are more positive employment signs (8% growth in workforce jobs 2001-2011).

What is driving this urban dynamic?

In addition to planning policy shifts, a series of economic and demographic changes are contributing to the pattern of central growth and struggling suburbs, as commentators have variously been observing in the UK and US (e.g. gentrification researchers, Erenhalt, Kochan). Demographic aspects include more students, immigrants, singles and childless couples. Economic aspects include city-centre friendly service and knowledge economy jobs, as well as increased costs of petrol. For these trends to occur over a wide range of demographically and economically diverse cities in the UK and beyond, clearly there are multiple factors pulling urban populations and growth in similar directions.

London Extremes

We’ve avoided the gigantic outlier of London so far. It’s a city apart in many ways- much larger (8.1 million in the GLA area) and faster growing (+14% 2001-2011). It’s also massively higher density, with average residents per hectare 50% higher (nearly 200 residents per hectare) than the next most dense city-region in GB. The biggest changes have been in Inner East London. Tower Hamlets (where Canary Wharf has boomed) is 1st on every indicator- highest population change (+28.8%), highest employment change (+50%!!), highest population density (324 residents / hectare). The pressures for growth in London are so high that there is little surburban decline in population terms (although employment has been declining significantly in Outer London).

Yet the high rate of densification in London has come nowhere near meeting housing demand. London is the midst of a massive housing shortage and crisis, with some of the world’s highest property prices. The debate is currently raging about what needs to be done to accelerate construction, with advocates of transforming more land to community ownership (e.g. Planners Network UK), relaxing planning regulations such as the green belt (e.g. LSE SERC), and implementing an array of measures simultaneously (e.g. Shelter Report). We can see London’s challenges in the maps, such as the failure thus far of the flagship housing expansion programme, the Thames Gateway, to deliver. Some high profile development sites like Stratford and Kings Cross have only recently opened for residents and so do not show in the 2011 data.

The Thames Gateway- aside from Woolwich, little housing has been delivered.

Another more surprising result is the fall in the population of Inner West London, particularly Kensington and Chelsea. While this finding does need some context- K&C is still the forth most densely populated local authority in the country- it’s still an amazing trend given the extreme population pressures in London. It is in line with arguments that the most expensive properties in London have become investments for international capital rather than homes for living. Such trends push prices up, cut supply and bring questionable benefits to the city. Addressing this issue would require tax changes, and macro economic factors like the value of the pound and yields on alternative investments are also clearly influential.

Inner London- expansion in the East and decline in Kensington & Chelsea

Summary- an Ongoing Renaissance and Suburban Challenges

Well to state the obvious GB cities are, with only a few exceptions, growing significantly. That’s not to be sniffed at given the history of widespread urban decline throughout the second half of the 20th century. And secondly the pattern of growth in density terms is clear- densifying inner cities, and fairly static or declining suburbs. The scale of London and the severe housing crisis has it’s own unique dynamics, while Glasgow and Liverpool are still dealing with significant population loss in many areas of the city region. But on the whole, the pattern is surprisingly consistent across cities in Great Britain.

Clearly this review prompts a series of further questions analysing the economic, demographic, gentrification, deprivation and property market processes inherent in this urban change, and what future city centres and suburbs will be like. Hopefully this mapping exercise should is a useful context for the ongoing research.

Our cities have been changing dramatically in recent years, with the intensification of urban centres, redevelopment of old industrial spaces, new demographic trends, and the pressures of a volatile global economy. The aim of the LuminoCity website, which launches in beta today, is to visualise urban form and dynamics to better understand how these trends are transforming cities in Great Britain. Explore the site for yourself here- luminocitymap.org.

London Population Density by Built-up Area 2011Glasgow Jobs Density by Built-up Area 2010Manchester Population Density Change 2001-2011

The visual style developed for LuminoCity combines urban activity data with built-form. Density values are calculated by dividing fine-scale (LSOA) employment and population data by built-up area, and then mapping the results to the same building footprint data (Ordnance Survey VectorMap). The result is a novel city perspective on common demographic indicators like population and employment density, with links between density and the texture of the built-environment clearly highlighted. So for example in the London map above, we can see the patchwork pattern of recent high density developments in Docklands (along the river to the east), and high density clustering around major rail stations like Paddington.

Each layer provides a complementary angle on urban form, with Employment Density showing business agglomeration patterns, and Population Density Change highlighting where intensification is occurring and where population losses are found. Examples of these three layers for major cities are shown above. The Population Density Change is particularly interesting in light of clear patterns of city centre growth and static or declining suburbs in many British cities, such as Manchester above. There is also in London a distinct pattern of population loss in the western inner-city, likely due to international capital speculation leaving under-occupied housing (see image below). These trends will be discussed in a further post later this week.

London Population Density Change 2001-2011

Multi-Scale Interactive Statistics

As well as browsing the map you can also click on particular locations to get a set of core statistics and rankings of that area for the current map layer. The statistics are at three spatial levels- City Region, Local Authority and LSOA. This feature shows how typical a particular area is compared to the wider city-region and the country as a whole. It also helps to communicate the variation in density measurements according to scale.

Location Statistics for Manchester, one of Britain’s fastest growing cities

The site concept was partly inspired by Ollie O’Brien’s ‘New Booth’ Map of Deprivation for Great Britain.

Datasets Used

The population data comes from the UK 2001 and 2011 Census, published by Office for National Statistics and National Records of Scotland. The employment data is derived from the Business Register and Employment Survey 2009-2011, also published by Office for National Statistics. The building footprint and urban area data is from the Ordnance Survey Vector District and Meridian products. These datasets have been published by the OS as Open Data, which is a fantastic recent development enabling sites like this to happen.

Spatial Analysis Method Details and Errors

All socio-economic mapping contains a degree of error, and the building footprint density approach used here introduces some issues. The Lower Super Output Area zone geography at which the population and employment data is published is fine scale but is not at the individual block level. Each LSOA zone represents groups of adjacent city blocks. The density results are therefore an average of the adjacent blocks in each zone. The results are affected by a particular version of the Modifiable Areal Unit Problem, and represent the density of fine-scale city neighbourhoods rather than of particular buildings. You can view the specific geography of the LSOA zones by turning on the ‘Admin Boundary’ layer on the LuminoCity site to see how blocks are aggregated.

Additionally the analysis does not consider building use (there are several technical and copyright challenges with this) and so population and employment density measures include all buildings rather than distinguishing residential and commercial property densities.

Finally, the ONS has not yet published census 2001 and 2011 population counts at the same LSOA geography, and a proportional spatial join method by building area was used to convert the 2001 LSOA census data to 2011 LSOAs for the Population Density Change layer.

Feedback and Comments

If you like the site or have any feedback or comments then you can tweet me @citygeographics, or email duncan2001@gmail.com. The site is in beta at the moment, and I plan to add more layers and interactivity in future releases. I’ll be blogging here in more detail about what the visualisations reveal about the changing geography of British cities over the coming weeks.

One of the most recognisable visualisation techniques used by LSE Cities in the Urban Age publications is the 3D density map- an intuitive and engaging way to represent built form, and enable comparison of very different city environments across the globe. I’ve been producing 3D density maps in my own research for around five years now, and so it was a nice challenge to produce the 3D density maps for this year’s Urban Age conference, the Electric City in London. In this post I focus on the contrasting densities in three leading world cities- London, New York and Hong Kong- with the added twist that both residential and employment densities are mapped for comparison.

Higher urban densities can facilitate more sustainable travel patterns, improve service delivery efficiency, reduce building energy use and promote urban vitality. These advantages depend of course on good urban planning to minimise congestion and pollution problems. High density mixed-use development is central to the compact city planning movement, and remains a foundation of sustainable planning policy today. Here we map the number of residents in each square kilometre of a 100 by 100 kilometre region for London, New York and Hong Kong. Lower urban densities apply to suburban-like neighbourhoods, while high densities generally represent medium or high rise buildings clustered on a tight urban grid.

The city that stands out in the mapping is Hong Kong, with its extremely high residential densities exceeding 110,000 people per km2. Here planners have responded to scarce land availability with very tall (over 30 storeys) high-density development. Scarce land has also influenced the development of New York City, where Manhattan densities peak at 59,000 people per km2. London in comparison is much lower density. The heritage of suburban housing and generous greenspace has created a residential culture at half the density of New York and a quarter the density of Hong Kong. Despite current intensification in London, residential densities remain a world away from other global cities.

Where people live is not however the only perspective needed to understand urban density. We can also examine employment densities for an important point of comparison (both residential and employment maps are at the same scale). Taller spikes in the employment maps represent higher numbers of jobs concentrated in business centres. London, New York and Hong Kong feature very intensive central employment clusters. The highest peak of over 150,000 jobs per km2 is in Midtown Manhattan. London is surprisingly close behind at over 140,000 jobs per km2, concentrated in the City of London and the West End. Hong Kong peaks at 120,000 jobs per km2 in Central (note the Hong Kong survey data is less comprehensive and may underestimate peak densities). These intense spikes represent very strong agglomeration economies, where financial and business services and creative industries cluster together to access labour markets, share fast-changing information and engage in face-to-face interaction with clients, customers and partners. Despite living in an age of instant telecommunication, proximity is still critical for many world city business activities.

The extreme employment density peaks are indicative of economic success in these world cities. Demand for office space is so stong that developers get sufficient returns to build high and businesses use their space more intensively. Central employment clustering also means these cities are dominated by public transport rather than car travel (particularly Hong Kong). On the other hand the divergence of living and working densities can signify a lack of integration between living and working locations. London is very polarised between its low density living and high density working environments. This contributes to the long distance and long duration commuting travel for many Londoners (recent surveys find an average one-way commute times for Londoners of 38 minutes). New York has a better integration of living and working locations (average commutes are around 31 minutes). Hong Kong appears to have the closest integration of living and working spaces, though unfortunately commuting time survey data is not available to test this.

The analysis here supports the medium-rise inner-city residential intensification that the London Plan prescribes to improve the balance of urban functions, and increase accessibility for residents and businesses. The gap in residential densities between London and many world cities is so large that modest intensification can be achieved while keeping London’s distinct character, providing development is on the much remaining brownfield land rather than London’s treasured greenspaces.

Another interesting thought is whether the highly concentrated office clusters we see in London and New York will continue to be the way most businesses operate in the future. Greg Lindsay gave a good talk last week on how businesses are changing the way they use work space towards more shared and flexible environments that will likely be less space demanding.

Cities that achieve social and economic success without high car use generally have three things in common: high densities, good urban design, and successful planning frameworks that integrate land-use with public transport, walking and cycling networks. I’ve been working on an LSE Cities project that investigated two leading global cities in green transport- Copenhagen and Hong Kong- to better understand how their leading positions were reached. You can read the final Going Green report here.

The project required visualising the level of integration between public transport and urban density in these cities. We developed a technique where the rail network is shown as a transect through a 3D population density surface. This shows how the density of jobs and residents in these cities is clustered around major public transport nodes.

Copenhagen has a classic radial pattern, based on the famous ‘Finger Plan‘ developed over 60 years ago, where linear urban features are separated by thin green wedges. This is quite distinct to the UK greenbelt approach. Current expansion is focussed to the south of the city centre along the Orestad corridor served by the more recently developed metro links. This area sites the airport and transport links to Sweden, continuing the cross-border integration between Copenhagen and Malmö.

Hong Kong makes a very interesting comparison. It is on average 8 times(!) higher density than Copenhagen, and peak densities are around four times higher at nearly 150,000 jobs & residents per square kilometre. This is due firstly to the natural boundaries and country park designations that prevent suburban development, and secondly to the unique ‘Rail plus Property’ planning model run by the government and MTR, where extremely high development densities are pursued at rail station sites, and land value gains captured to fund public transport. The result is a polycentric pattern of jagged nodal development.

Another way to consider this relationship is to measure typical distances to rail & metro stations for these cities. As can be seen below, Copenhagen and Hong Kong compare favourably to other leading global cities like London and New York.

It would be interesting to pursue this analysis further for London. You can see that London scores relatively lower for the population within 500 metres of stations. Intensification policies at public transport nodes are a recent policy change for London. Accessibility figures are likely to change over time with several major intensification projects under way at rail stations in Inner London.

(Above figure based on metropolitan regions. Defined as Outer Met Area for London and 100 km by 100 km square centred on Manhattan for NYC).

The structure of large cities such as London is complex and endlessly fascinating. Effective visualisation can reveal the many patterns in urban structures for research and planning tasks, and the visualisation challenge is to manage the multi-dimensional and dynamic nature of urban complexity. Here we explore the geography of land-use and density across Greater London using 3D cartography at a 500 metre grid scale (HD version here):

London is highly centralised, with recent patterns of intensification in the City of London, Canary Wharf and Inner London more generally cementing this pattern. Meanwhile much of Outer London struggles to attract higher value commercial uses. We will explore the agglomeration, property market, and planning policy processes that underlie these trends in future posts.

Many of land use patterns visible in London resemble the ‘classic’ urban location theory models: there is an extreme Alonso-type density gradient; retail uses retain a central-place hierarchy; and there are distinct radial corridors. Additionally further theories on the economics of mix-of-uses (e.g. Jacobs) and the lumpy mega-scale of real-estate investment are clearly key parts of London’s make-up.

The London Urban Form movie was created in ArcGlobe, which has some nice features like the ability to change the background mapping and animation timeline features. The advantages of doing the movie within GIS is the ability to easily combine spatial data at a variety of scales. Some of the more advanced animation effects that I would like to use such as geometry transitions (to show growth and decline) and controlling lighting are however not possible in GIS. A previous visualisation of this data in 3DS Max by Andy-Hudson Smith shows how these effects can be achieved.